Xue Cai, Yi Sun, Dingcun Luo, Hao Zhang, Wenjun Wei, Y I Zhang, Weigang Ge, Jing Liu, Guan Ruan, Lu Li, Lingling Tan, Luang Xu, Genqi Xu, Haixia Guan, Jiang Zhu, Wanyuan Chen, Qiushi Zhang, Huijuan Si, Juntao Qiu, Zhangzhi Xue, Zhiqiang Gui, Haitao Zheng, Gai Wu, Yu Wang, Lei Xie, Guang Chen, Guoyang Wu, Lei Liang, Chuang Chen, Xianghui He, Meiping Shen, Jianbiao Wang, M H Ge, Deguang Zhang, Liang Zhou, Yijun Wu, Hui Shi, Fan Wu, Zhihong Wang, Hong Han, Jing Yao, Zhiyan Liu, Mingzhao Xing, Kennichi Kakudo, Yibo Gao, Wen Tian, Huixiong Xu, Yi Zhu, Yu Wang, Tiannan Guo
Accurate preoperative diagnosis of thyroid nodules via fine-needle aspiration (FNA) biopsy remains challenging, particularly in cases with indeterminate cytology. This prospective, noninterventional, blinded, multicenter study establishes ThyroProt, a diagnostic classifier that integrates targeted mass-spectrometry-based quantification of a 3-protein signature with BRAF V600E mutation status, age, and gender. Developed and validated on 837 FNA samples, the classifier is evaluated in a prospective test set of 322 samples, achieving an area under the curve (AUC) of 0.94 with an overall accuracy of 90.7%. For the critical subgroup of Bethesda III/IV nodules, ThyroProt demonstrates an accuracy of 88.0%, with 82.4% sensitivity and 100% specificity. The classifier's robust performance is further evaluated in two independent multicenter cohorts, where it maintains an AUC of 0.87-0.91 and an accuracy of 84.3%-85.7%. This study supports the clinical utility of mass-spectrometry-based targeted proteomics for improving preoperative diagnosis of thyroid nodules, particularly those with indeterminate cytology.